alexa Statistical methods to analyze adverse events data of randomized clinical trials.
Mathematics

Mathematics

Journal of Biometrics & Biostatistics

Author(s): Siddiqui O

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Abstract The adverse events data of randomized clinical trials are often analyzed based on either crude incidence rates or exposure-adjusted incidence rates. These rates do not adequately account for an individual patient's profile of adverse events over the study period when an individual may remain in the trial after experiencing one or more events (i.e., occurrence of multiple events of the same kind or different kinds). Moreover, the required statistical assumptions (e.g., constant hazard rate over time) for valid estimates of incidence rates are not likely to be met in practice by adverse events data of clinical trials. A nonparametric approach called the mean cumulative function (MCF) provides a valid statistical inference on recurrent adverse event profiles of drugs in randomized clinical trials. The estimate involves no assumptions about the form of MCF. To demonstrate the applicability and utility of the MCF approach in clinical trial datasets, an adverse event dataset obtained from a clinical trial is analyzed in this article. As compared to the crude or exposure-adjusted incidence rates of adverse events, the MCF estimates facilitate more understanding of safety profiles of a drug in a randomized clinical trial. This article was published in J Biopharm Stat and referenced in Journal of Biometrics & Biostatistics

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